A bilevel optimization model and a PSO-based algorithm in day-ahead electricity markets

Authors:
Guoli Zhang;Guangquan Zhang;Ya Gao;Jie Lu
Affiliations:
Department of Mathematics and Physics, North China Electric Power University, Hebei, P.R.China;Faculty of Engineering & Information Technology, University of Technology, Sydney, NSW, Australia;Faculty of Engineering & Information Technology, University of Technology, Sydney, NSW, Australia;Faculty of Engineering & Information Technology, University of Technology, Sydney, NSW, Australia
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

Strategic bidding problems are becoming key issues in competitive electricity markets. This paper applies bilevel optimization theory to deal with this issue. We first analyze generating company strategic bidding behaviors and build a bilevel optimization model for a day-ahead electricity market. In this bilevel optimization model, each generating company will choose their bids in order to maximize their individual profits. A market operator will determine the output power for each unit and uniform marginal price based on the minimization purchase electricity fare. For solving this competitive strategic bidding problem described by the bilevel optimization model, a particle swarm optimization (PSO)-based algorithm is. Experiment results have demonstrated the validity of the PSO-based algorithm in solving the competitive strategic bidding problems for a day-ahead electricity market.